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Reduced Texture Based Features’ Approach for Identification of Different Views of Brain MRI Images

Basavaraj S. Anami, Prakash H. Unki


This paper gives classification of brain MRI images as coronal, sagittal and axial. Three different types of texture features sets, namely, Gray Level Co-occurrence Matrices (GLCM), Gray Level Run-Length Matrices (GLRM), and statistical texture (ST) features are deployed. Features are reduced based on performances of individual features. The reduced features set is used to train a Back Propagation Neural Network (BPNN). The experiments are conducted by combining GLCM, GLRM and ST features to improve the classification accuracy. The observed classification accuracy is 100% across all the MRI brain image views for the combination of GLCM and ST features.


Classification, Brain MRI, BPNN, ST, GLCM, GLRM.

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